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The Random Forest model (a powerful and versatile learning algorithm primarily used for classification and regression tasks that operates by constructing multiple decision trees and combining their predictions to improve accuracy and reduce overfitting) outperformed others, with 90.2% sensitivity and 76.7% specificity, identifying PORH parameters and diabetes duration as critical predictors [53]

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